Brandon Amos is a Research Scientist at Meta Superintelligence Labs in NYC. He holds a PhD in Computer Science from Carnegie Mellon University where he was supported by the NSF Graduate Research Fellowship. Prior to joining Meta, he also worked at Adobe Research, Google DeepMind, and Intel Labs, and served as a visiting lecturer at Cornell Tech. He has received best paper awards at the ICML Theoretical Foundations Workshop and ACM MMSys, best reviewer awards at NeurIPS, ICML, ICLR, and AISTATS, and is also an area chair for NeurIPS and AAAI. His research focuses on foundational topics spanning machine learning, optimization, reinforcement learning, and control, with the goal of building safe intelligent systems that understand and interact with our world. Recently he has been focusing on language modeling and diffusion/flow-based generative modeling. Major themes of his research involve attacking language models to improve safety and alignment, improving RL and control systems, applied optimal transport and flows, amortization and meta-learning between tasks, and integrating structural information and domain knowledge into AI systems through differentiable optimization.